AIMC Topic: Lung Neoplasms
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A deep learning-based framework (Co-ReTr) for auto-segmentation of non-small cell-lung cancer in computed tomography images.
Journal of applied clinical medical physics
Feb 19, 2024
PURPOSE: Deep learning-based auto-segmentation algorithms can improve clinical workflow by defining accurate regions of interest while reducing manual labor. Over the past decade, convolutional neural networks (CNNs) have become prominent in medical ...
Comparing Robotic, Thoracoscopic, and Open Segmentectomy: A National Cancer Database Analysis.
The Journal of surgical research
Feb 15, 2024
INTRODUCTION: Minimally invasive approaches to lung resection have become widely acceptable and more recently, segmentectomy has demonstrated equivalent oncologic outcomes when compared to lobectomy for early-stage non-small cell lung cancer (NSCLC)....
Potential of radiomics analysis and machine learning for predicting brain metastasis in newly diagnosed lung cancer patients.
Clinical radiology
Feb 13, 2024
AIM: To explore the potential of utilising radiomics analysis and machine-learning models that incorporate intratumoural and peritumoural regions of interest (ROIs) for predicting brain metastasis (BM) in newly diagnosed lung cancer patients.
Meta-lasso: new insight on infection prediction after minimally invasive surgery.
Medical & biological engineering & computing
Feb 13, 2024
Surgical site infection (SSI) after minimally invasive lung cancer surgery constitutes an important factor influencing the direct and indirect economic implications, patient prognosis, and the 5-year survival rate for early-stage lung cancer patients...
METnet: A novel deep learning model predicting MET dysregulation in non-small-cell lung cancer on computed tomography images.
Computers in biology and medicine
Feb 13, 2024
BACKGROUND: Mesenchymal epithelial transformation (MET) is a key molecular target for diagnosis and treatment of non-small cell lung cancer (NSCLC). The corresponding molecularly targeted therapeutics have been approved by Food and Drug Administratio...
Overcoming the Challenge of Accurate Segmentation of Lung Nodules: A Multi-crop CNN Approach.
Journal of imaging informatics in medicine
Feb 12, 2024
Lung nodules are generated based on the growth of small and round- or oval-shaped cells in the lung, which are either cancerous or non-cancerous. Accurate segmentation of these nodules is crucial for early detection and diagnosis of lung cancer. Howe...
Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study.
Journal of imaging informatics in medicine
Feb 12, 2024
The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patient...
Artificial Intelligence-Guided Segmentation and Path Planning Software for Transthoracic Lung Biopsy.
Journal of vascular and interventional radiology : JVIR
Feb 12, 2024
PURPOSE: To validate the sensitivity and specificity of a 3-dimensional (3D) convolutional neural network (CNN) artificial intelligence (AI) software for lung lesion detection and to establish concordance between AI-generated needle paths and those u...
ADGAN: Attribute-Driven Generative Adversarial Network for Synthesis and Multiclass Classification of Pulmonary Nodules.
IEEE transactions on neural networks and learning systems
Feb 5, 2024
Lung cancer is the leading cause of cancer-related deaths worldwide. According to the American Cancer Society, early diagnosis of pulmonary nodules in computed tomography (CT) scans can improve the five-year survival rate up to 70% with proper treatm...
Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation.
Physics in medicine and biology
Jan 24, 2024
. Prior to radiation therapy planning, accurate delineation of gross tumour volume (GTVs) and organs at risk (OARs) is crucial. In the current clinical practice, tumour delineation is performed manually by radiation oncologists, which is time-consumi...
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